Sedimentary Coastal Cliff Erosion in Greenland
Bibliographic record
Abstract
Abstract Climate change will increase the duration of annual sea‐ice‐free periods and shift precipitation patterns across the Arctic. Those factors are likely to increase erosion rates along its coasts. Large parts of the Arctic coast consist of hard rock. However, glacial, deltaic, and coastal sedimentary deposits occur in deglaciated areas and isostatic uplift following glaciations has created beach ridge plains and pocket beaches with coarse soft‐sediment cliffs. Hitherto, very little was known about the spatial distribution, erosion rates, and morphodynamics of soft sediment cliffs along the coast of Greenland. Here, we investigate a 3‐km sedimentary cliff section on the south coast of Qeqertarsuaq (Disko Island). We measured 2D cliff top erosion over 50 years between 1964 and 2014 as well as 3D cliff profile change over 2 years between 2019 and 2021. Morphometric indices of the gravel beach and cliff were calculated based on a series of cross‐shore elevation profiles. Wave run‐up at the beach fronting the cliff was modeled with XBeach‐G for a series of storm events under present day sea‐ice conditions and for a reduced sea‐ice scenario. Cliff top erosion rates varied along the cliff with maximum rates of 0.3 m y−1. The investigated coastal cliff erodes by two coupled processes: (a) precipitation‐driven surface runoff downslope the cliff and (b) wave‐driven erosion at the cliff toe. In a continuously warming climate, this study shows that erosion of soft coastal cliffs in Greenland thus can accelerate due to increased storminess and prolonging open water periods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".